Unsupervised learning is used mainly with unlabeled dataset. The
model supported by this learning finds patterns from previous
sequence or dataset and identifies or predicts the next one. For
exploratory analysis, the unsupervised learning method is used to
automate the identification pattern of data structures. Using an
unsupervised method, one can reduce the dimensions used to
represent data for fewer columns and features. To sort the
eigenvectors, Principal Component Analysis (PCA) [1]} is
used to compute the eigenvectors of the co-variance matrix that is
“principal axes”. To get the dimensionality reduction, the
centered data were projected into principal axes. Principal
component (PC) scores are a group of score that are obtained from
PCA. To create equal number of new imaginary variables or
principle components the relationship between a batch or group of
those PC scores are analyzed. The optimized and maximally
correlated with all of the original group of variables is the
first created imaginary variables, then the next created imaginary
variable is less correlated and next is lesser than the previous
and it goes on until the point when the principal components
scores predicts any variable from the first created group.
